It is hard for artificial agents to quickly adapt to new things/data like human intelligence, since it is challenging to integrate the prior experience with a few more new information. Meta-learning, or learning to learn, is the mechanism proposed for the learning-based problems, which is usually used in few-shot/zero-shot learning and transfer learning. In meta-learning, the trained model quickly learns a new task in large task space, where the test samples are used to optimize the meta-learner, therefore the model can quickly adapt with the help of the meta-learner when it encounters new tasks. In SISR, considering the lack of real paired samples, we hope that the model can be trained on simulated paired datasets and then transfer the learned experience to the real SISR task. To address this issue, Soh et al. proposed the MZSR [1]}. In MZSR, a novel training scheme based on meta-transfer learning is proposed to learn an effective initial weight for fast adaptation to new tasks with the zero-shot unsupervised setting, thus the model can be applied to the real-world scenarios and achieve good results. In [2]}, Park et al. proposed an effective meta-learning method to further improve the model performance without changing the architecture of conventional SISR networks. This method can be applied to any existing SISR models and effectively handle unknown SR kernels. In [3]}, Hu et al. proposed the first unified super-resolution network for arbitrary degradation parameters with meta-learning, termed Meta-USR [3]}.
